Grooming of Symmetric Traffic in Unidirectional SONET/WDM Rings
Bibliographic record
Abstract
In SONET/WDM networks, a wavelength channel is shared by multiple low-rate traffic demands. The multiplexing is known as traffic grooming and realized by SONET add-drop multiplexers (SADM). The grooming factor is the maximum number of low-rate traffic demands that can be multiplexed in one wavelength. Since SADMs are expensive, a key optimization problem in traffic grooming is to minimize the number of SADMs. This optimization problem is challenging and NP-hard even for unidirectional SONET/WDM ring networks with symmetric unitary traffic demands. In this paper, we propose an algorithm for this NP-hard problem. For a set R of symmetric pairs of unitary traffic demands on a SONET ring with n nodes, and a grooming factor of k, our algorithm grooms R into [|R|]/k wavelengths using at most [(1 + 1/k)|R|] + [n/4] SADMs. It can be proved that there exists an instance whose optimal solution requires as many as (1 + 1/k)|R| + |R|/2k SADMs, which is very close to our upper bound. For the guaranteed performance, our algorithm achieves a better approximation ratio than previous ones. Our algorithm uses the minimum number of wavelengths that are also precious resources in optical networks. In addition, the experimental results show that our algorithm has much better practical performance than the previous algorithms in most cases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".